Teachers’ Use of Predictive Learning Analytics: Experiences from The Open University UK

Gillespie, Anna (2022). Teachers’ Use of Predictive Learning Analytics: Experiences from The Open University UK. EdD thesis The Open University.

DOI: https://doi.org/10.21954/ou.ro.000151fa


This thesis evaluates the role of predictive learning analytics (PLA) as a teaching tool in higher education. The focus is the Open University’s (OU) Associate Lecturers (ALs). The OU is an online distance learning university in the UK where PLA presented in the form of the Early Alert Indicators (EAI) dashboard which has been available to ALs for over two years. However existing research suggests that it is not well adopted.

The thesis aims to give a better understanding of the reasons for teachers’ resistance to using PLA. It addresses the following research questions: 1) How do existing teacher beliefs relate to Associate Lecturers’ use of PLA? 2) How does (a) knowledge of technology and (b) data literacy relate to Associate Lecturers’ use of PLA? 3) What are Associate Lecturers’ (a) perceptions of the use and usability of PLA and (b) actual use as measured by fine-grained eye-tracking approaches and observation of use?

Three theoretical models informed research questions: The Theory of Planned Behaviour (Ajzen 1991): The Decomposed Theory of Planned behaviour (Taylor and Todd, 1995); and the Unified Theory of Acceptance and Use of Technology (Venkatesh, Morris, and Davis, 2003).

Using a mixed methods approach, three strands of enquiry were synthesised: Semi- structured interviews with eleven (N=11) participants. Of the 11 participants, six (n=6) participated in a fine-grained observation of EAI use using eye-tracking and retrospective think aloud protocols and five (n=5) participated in a screen-sharing observation and concurrent think aloud protocols.

Findings identified a range of reasons for low engagement with EAI including levels of management support, ethical concerns about use of student data, and evidence of some erroneous interpretations of EAI as identified by the analysis of eye-tracking data and screen-sharing observation.

Conclusion: The support of management, a clear understanding of the ethical basis of EAI and ongoing training are key to the adoption of PLA in online and distance learning in higher education. While theoretical models are useful for understanding technology use, they are limited in their application for PLA and a new model of Technology and Data Acceptance in Education (PLA) is proposed.

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